Update app.py

app.py

@@ -154,32 +154,45 @@ def remove_file(n_clicks, existing_files):
     return [file for file in existing_files if file['props']['children'][1]['props']['children'] != removed_file]
 
 def generate_matrix_with_gpt(matrix_type, file_contents):
-    prompt = f"Generate a {matrix_type} based on the following project artifacts:\n\n"
-    prompt += "\n\n".join(file_contents)
-    prompt += f"\n\nCreate a {matrix_type} in a format that can be represented as a pandas DataFrame. Use '|' to separate columns and ensure each row has the same number of columns."
+    prompt = f"""Generate a {matrix_type} based on the following project artifacts:
+
+{' '.join(file_contents)}
+
+Instructions:
+1. Create the {matrix_type} as a table.
+2. Use ONLY pipe symbols (|) to separate columns.
+3. Do NOT include any introductory text, descriptions, or explanations.
+4. Do NOT use any dashes (-) or other formatting characters.
+5. The first row should be the column headers.
+6. Start the output immediately with the column headers.
+7. Each subsequent row should represent a single item in the matrix.
+
+Example format:
+Header1|Header2|Header3
+Item1A|Item1B|Item1C
+Item2A|Item2B|Item2C
+
+Now, generate the {matrix_type}:
+"""
 
     response = openai.ChatCompletion.create(
         model="gpt-3.5-turbo",
         messages=[
-            {"role": "system", "content": "You are a helpful assistant that generates project management matrices."},
+            {"role": "system", "content": "You are a precise matrix generator that outputs only the requested matrix without any additional text."},
             {"role": "user", "content": prompt}
         ]
     )
 
-    matrix_text = response.choices[0].message.content
-    
-    # More robust parsing of the matrix text
-    lines = [line.strip() for line in matrix_text.strip().split('\n') if line.strip()]
+    matrix_text = response.choices[0].message.content.strip()
+    print("Raw matrix text from GPT:", matrix_text)  # For debugging
+
+    lines = [line.strip() for line in matrix_text.split('\n') if '|' in line]
     data = [line.split('|') for line in lines]
-    
-    # Ensure all rows have the same number of columns
-    max_columns = max(len(row) for row in data)
-    data = [row + [''] * (max_columns - len(row)) for row in data]
-    
-    # Use the first row as headers, and the rest as data
+    data = [[cell.strip() for cell in row] for row in data]
+
     headers = data[0]
     data = data[1:]
-    
+
     return pd.DataFrame(data, columns=headers)
 
 @app.callback(
@@ -220,38 +233,41 @@ def update_matrix_via_chat(n_clicks, chat_input):
     if not chat_input or current_matrix is None:
         raise PreventUpdate
     
-    prompt = f"Update the following {matrix_type} based on this instruction: {chat_input}\n\n"
-    prompt += current_matrix.to_string(index=False)
-    prompt += "\n\nProvide the updated matrix using '|' to separate columns. Do not include any separator lines, headers, or formatting characters. Start directly with the column names separated by '|'."
+    prompt = f"""Update the following {matrix_type} based on this instruction: {chat_input}
+
+Current matrix:
+{current_matrix.to_string(index=False)}
+
+Instructions:
+1. Provide ONLY the updated matrix as a table.
+2. Use ONLY pipe symbols (|) to separate columns.
+3. Do NOT include any introductory text, descriptions, or explanations.
+4. Do NOT use any dashes (-) or other formatting characters.
+5. The first row should be the column headers.
+6. Start the output immediately with the column headers.
+7. Each subsequent row should represent a single item in the matrix.
+
+Now, provide the updated {matrix_type}:
+"""
     
     response = openai.ChatCompletion.create(
         model="gpt-3.5-turbo",
         messages=[
-            {"role": "system", "content": "You are a helpful assistant that updates project management matrices without any formatting or separator lines."},
+            {"role": "system", "content": "You are a precise matrix updater that outputs only the requested matrix without any additional text."},
             {"role": "user", "content": prompt}
         ]
     )
 
-    updated_matrix_text = response.choices[0].message.content
+    updated_matrix_text = response.choices[0].message.content.strip()
     print("Raw updated matrix text from GPT:", updated_matrix_text)  # For debugging
 
-    # Filter out any lines that don't contain the '|' character
-    lines = [line.strip() for line in updated_matrix_text.strip().split('\n') if '|' in line]
-    
-    # Parse the matrix text
+    lines = [line.strip() for line in updated_matrix_text.split('\n') if '|' in line]
     data = [line.split('|') for line in lines]
-    
-    # Strip whitespace from each cell
     data = [[cell.strip() for cell in row] for row in data]
-    
-    # Ensure all rows have the same number of columns
-    max_columns = max(len(row) for row in data)
-    data = [row + [''] * (max_columns - len(row)) for row in data]
-    
-    # Use the first row as headers, and the rest as data
+
     headers = data[0]
     data = data[1:]
-    
+
     current_matrix = pd.DataFrame(data, columns=headers)
     
     return f"Matrix updated based on: {chat_input}", dbc.Table.from_dataframe(current_matrix, striped=True, bordered=True, hover=True)